Fitbeat: COVID-19 Estimation based on Wristband Heart Rate

TL;DR

This paper presents a deep learning approach using wristband heart rate data to detect COVID-19 infection, achieving high accuracy and sensitivity in a cohort of MS patients.

Contribution

It introduces a contrastive convolutional auto-encoder that outperforms traditional CNNs in COVID-19 detection from wearable heart rate data.

Findings

Achieved 95.3% unweighted average recall

Attained 100% sensitivity in COVID-19 detection

Outperformed conventional CNN and auto-encoder models

Abstract

This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data. The study utilises data from the ongoing EU IMI RADAR-CNS research project that is investigating the feasibility of wearable devices and smart phones to monitor individuals with multiple sclerosis (MS), depression or epilepsy. Aspart of the project protocol, heart-rate data was collected from participants using a Fitbit wristband. The presence of COVID-19 in the cohort in this work was either confirmed through a positive swab test, or inferred through the self-reporting of a combination of symptoms including fever, respiratory symptoms, loss of smell or taste, tiredness and gastrointestinal symptoms. Experimental results indicate that our proposed contrastive convolutional auto-encoder (contrastive CAE), i. e., a combined architecture of an auto-encoder and contrastive loss, outperforms a conventional convolutional neural network (CNN), as well as a convolutional auto-encoder (CAE) without using contrastive loss. Our final contrastive CAE achieves 95.3% unweighted average recall, 86.4% precision, anF1 measure of 88.2%, a sensitivity of 100% and a specificity of 90.6% on a testset of 19 participants with MS who reported symptoms of COVID-19. Each of these participants was paired with a participant with MS with no COVID-19 symptoms.